Curricula

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

The course will be implemented in the spring semester of 2024.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials. Assignments are mainly done as group work.

Practical training and working life connections

The aim is to connect the content of the course to problems that occur in working life.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Further information for students

The course is evaluated on the basis of the assignments, which must be returned by the given timetables.
The assessment methods are reviewed at the beginning of the course.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

The course will be implemented in the fall semester of 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtual study including doing assignments and familiarizing yourself with related lecture and example materials. Assignments are mainly done as group work.

Practical training and working life connections

The aim is to connect the content of the course to problems that occur in working life.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.

Further information for students

The course is evaluated on the basis of the assignments, which must be returned by the given timetables.
The assessment methods are reviewed at the beginning of the course.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Further information for students

Arviointimenetelmät käydään läpi opintojakson alussa.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Further information for students

Arviointimenetelmät käydään läpi opintojakson alussa.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

Opintojakso toteutetaan syyslukukaudella 2022.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Further information for students

Arviointimenetelmät käydään läpi opintojakson alussa.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Time and location

Opintojakso toteutetaan syyslukukaudella 2022.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Further information for students

Arviointimenetelmät käydään läpi opintojakson alussa.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.

Objectives

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Evaluation criteria, good (3-4)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Evaluation criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.